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28 pages, 10144 KiB  
Article
Decoding the Spatial–Temporal Coupling Dynamics of Land Use Intensity and Balance in China’s Chengdu–Chongqing Economic Circle: A 1 km Grid-Based Analysis
by Zijia Yan, Chenxi Zhou, Ziyi Tang, Hanfei Wang and Hao Li
Land 2025, 14(8), 1597; https://doi.org/10.3390/land14081597 - 5 Aug 2025
Abstract
Amid China’s national strategic prioritization of the Chengdu–Chongqing Economic Circle and accelerated territorial spatial planning, this study deciphered the synergistic evolution of Land Use Intensity (LUI) and Balance Degree of Land Use Structure (BDLUS) during rapid urbanization. Leveraging 1 km grid units and [...] Read more.
Amid China’s national strategic prioritization of the Chengdu–Chongqing Economic Circle and accelerated territorial spatial planning, this study deciphered the synergistic evolution of Land Use Intensity (LUI) and Balance Degree of Land Use Structure (BDLUS) during rapid urbanization. Leveraging 1 km grid units and integrating emerging spatiotemporal hotspot analysis, BFAST, and geographic detectors, we systematically analyzed spatiotemporal patterns and drivers of LUI, BDLUS, and their Coupling Coordination Degree (CCD) from 2000 to 2022. Key findings: (1) LUI strongly correlated with economic growth, with core areas reaching high-intensity development (average > 2.96) versus ecologically constrained marginal zones (<2.42), marked by abrupt changes during 2011–2014; (2) BDLUS improvements covered 82.22% of the study area, driven by the Yangtze River Economic Belt strategy (21.96% hotspot concentration), yet structural imbalance persisted in transitional zones (18.81% cold spots); (3) CCD exhibited center-edge dichotomy, contrasting high-value cores (CCD > 0.68) with ecologically sensitive edges (9.80% cold spots), peaking in regulatory shifts around 2010; (4) terrain constraints and intensified human activities (the interaction effect between nighttime lighting and population density increased by 219.49% after 2020) jointly governed coupling mechanisms, with urbanization and industrial transition becoming dominant drivers. This research advances an “intensity–structure–coordination” framework and elucidates “dual-core resonance” dynamics, offering theoretical foundations for spatial optimization and ecological civilization. Full article
(This article belongs to the Special Issue Integration of Remote Sensing and GIS for Land Use Change Assessment)
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35 pages, 4098 KiB  
Article
Prediction of Earthquake Death Toll Based on Principal Component Analysis, Improved Whale Optimization Algorithm, and Extreme Gradient Boosting
by Chenhui Wang, Xiaotao Zhang, Xiaoshan Wang and Guoping Chang
Appl. Sci. 2025, 15(15), 8660; https://doi.org/10.3390/app15158660 (registering DOI) - 5 Aug 2025
Abstract
Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges [...] Read more.
Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R2) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response. Full article
(This article belongs to the Section Earth Sciences)
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21 pages, 3832 KiB  
Article
Effects of Water Use Efficiency Combined with Advancements in Nitrogen and Soil Water Management for Sustainable Agriculture in the Loess Plateau, China
by Hafeez Noor, Fida Noor, Zhiqiang Gao, Majed Alotaibi and Mahmoud F. Seleiman
Water 2025, 17(15), 2329; https://doi.org/10.3390/w17152329 - 5 Aug 2025
Abstract
In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among [...] Read more.
In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha−1 (N0), 90 kg·ha−1 (N90), 180 kg·ha−1 (N180), 210 kg·ha−1 (N210), and 240 kg·ha−1 (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (Tmax, Tmin, Taver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article
(This article belongs to the Special Issue Soil–Water Interaction and Management)
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14 pages, 5995 KiB  
Article
Integrated Remote Sensing Evaluation of Grassland Degradation Using Multi-Criteria GDCI in Ili Prefecture, Xinjiang, China
by Liwei Xing, Dongyan Jin, Chen Shen, Mengshuai Zhu and Jianzhai Wu
Land 2025, 14(8), 1592; https://doi.org/10.3390/land14081592 - 4 Aug 2025
Abstract
As an important ecological barrier and animal husbandry resource base in arid and semi-arid areas, grassland degradation directly affects regional ecological security and sustainable development. Ili Prefecture is located in the western part of Xinjiang, China, and is a typical grassland resource-rich area. [...] Read more.
As an important ecological barrier and animal husbandry resource base in arid and semi-arid areas, grassland degradation directly affects regional ecological security and sustainable development. Ili Prefecture is located in the western part of Xinjiang, China, and is a typical grassland resource-rich area. However, in recent years, driven by climate change and human activities, grassland degradation has become increasingly serious. In view of the lack of comprehensive evaluation indicators and the inconsistency of grassland evaluation grade standards in remote sensing monitoring of grassland resource degradation, this study takes the current situation of grassland degradation in Ili Prefecture in the past 20 years as the research object and constructs a comprehensive evaluation index system covering three criteria layers of vegetation characteristics, environmental characteristics, and utilization characteristics. Net primary productivity (NPP), vegetation coverage, temperature, precipitation, soil erosion modulus, and grazing intensity were selected as multi-source indicators. Combined with data sources such as remote sensing inversion, sample survey, meteorological data, and farmer survey, the factor weight coefficient was determined by analytic hierarchy process. The Grassland Degeneration Comprehensive Index (GDCI) model was constructed to carry out remote sensing monitoring and evaluation of grassland degradation in Yili Prefecture. With reference to the classification threshold of the national standard for grassland degradation, the GDCI grassland degradation evaluation grade threshold (GDCI reduction rate) was determined by the method of weighted average of coefficients: non-degradation (0–10%), mild degradation (10–20%), moderate degradation (20–37.66%) and severe degradation (more than 37.66%). According to the results, between 2000 and 2022, non-degraded grasslands in Ili Prefecture covered an area of 27,200 km2, representing 90.19% of the total grassland area. Slight, moderate, and severe degradation accounted for 4.34%, 3.33%, and 2.15%, respectively. Moderately and severely degraded areas are primarily distributed in agro-pastoral transition zones and economically developed urban regions, respectively. The results revealed the spatial and temporal distribution characteristics of grassland degradation in Yili Prefecture and provided data basis and technical support for regional grassland resource management, degradation prevention and control and ecological restoration. Full article
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35 pages, 1129 KiB  
Article
Internal and External Cultivation to Drive Enterprises’ Green Transformation: Dual Perspectives of Vertical Supervision and Environmental Self-Discipline
by Huixiang Zeng, Yuyao Shao, Ning Ding, Limin Zheng and Jinling Zhao
Sustainability 2025, 17(15), 7062; https://doi.org/10.3390/su17157062 - 4 Aug 2025
Abstract
Central Environmental Protection Inspection (CEPI) is a major step in China’s environmental vertical supervision reform. With the multi-period Difference-in-Differences method, we assess the impact of CEPI on enterprise green transformation. In addition, we further explore the impact of enterprise environmental self-discipline. The results [...] Read more.
Central Environmental Protection Inspection (CEPI) is a major step in China’s environmental vertical supervision reform. With the multi-period Difference-in-Differences method, we assess the impact of CEPI on enterprise green transformation. In addition, we further explore the impact of enterprise environmental self-discipline. The results show that CEPI significantly promotes enterprise green transformation, and this effect on governance is further strengthened by environmental self-discipline. The synergistic governance effect of compound environmental regulation is pronounced, particularly in enterprises lacking government–enterprise relationships and in areas covered by CEPI “look back” initiatives and where local governments rigorously enforce environmental laws. The mechanism analysis reveals that CEPI mainly promotes enterprise green transformation by improving executive green cognition, boosting investment in environmental protection, and enhancing green innovation efficiency. This study provides a fresh perspective on analyzing the governance impact of CEPI and provides valuable insights for improving multi-collaborative environmental governance systems. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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25 pages, 28131 KiB  
Article
Landslide Susceptibility Assessment in Ya’an Based on Coupling of GWR and TabNet
by Jiatian Li, Ruirui Wang, Wei Shi, Le Yang, Jiahao Wei, Fei Liu and Kaiwei Xiong
Remote Sens. 2025, 17(15), 2678; https://doi.org/10.3390/rs17152678 - 2 Aug 2025
Viewed by 328
Abstract
Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes [...] Read more.
Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article
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21 pages, 6621 KiB  
Article
Ecological Restoration Reshapes Ecosystem Service Interactions: A 30-Year Study from China’s Southern Red-Soil Critical Zone
by Gaigai Zhang, Lijun Yang, Jianjun Zhang, Chongjun Tang, Yuanyuan Li and Cong Wang
Forests 2025, 16(8), 1263; https://doi.org/10.3390/f16081263 - 2 Aug 2025
Viewed by 179
Abstract
Situated in the southern hilly-mountain belt of China’s “Three Zones and Four Belts Strategy”, Gannan region is a critical ecological shelter belt for the Ganjiang River. Decades of intensive mineral extraction and irrational agricultural development have rendered it into an ecologically fragile area. [...] Read more.
Situated in the southern hilly-mountain belt of China’s “Three Zones and Four Belts Strategy”, Gannan region is a critical ecological shelter belt for the Ganjiang River. Decades of intensive mineral extraction and irrational agricultural development have rendered it into an ecologically fragile area. Consequently, multiple restoration initiatives have been implemented in the region over recent decades. However, it remains unclear how relationships among ecosystem services have evolved under these interventions and how future ecosystem management should be optimized based on these changes. Thus, in this study, we simulated and assessed the spatiotemporal dynamics of five key ESs in Gannan region from 1990 to 2020. Through integrated correlation, clustering, and redundancy analyses, we quantified ES interactions, tracked the evolution of ecosystem service bundles (ESBs), and identified their socio-ecological drivers. Despite a 31% decline in water yield, ecological restoration initiatives drove substantial improvements in key regulating services: carbon storage increased by 6.9 × 1012 gC while soil conservation rose by 4.8 × 108 t. Concurrently, regional habitat quality surged by 45% in mean scores, and food production increased by 2.1 × 105 t. Critically, synergistic relationships between habitat quality, soil retention, and carbon storage were progressively strengthened, whereas trade-offs between food production and habitat quality intensified. Further analysis revealed that four distinct ESBs—the Agricultural Production Bundle (APB), Urban Development Bundle (UDB), Eco-Agriculture Transition Bundle (ETB), and Ecological Protection Bundle (EPB)—were shaped by slope, forest cover ratio, population density, and GDP. Notably, 38% of the ETB transformed into the EPB, with frequent spatial interactions observed between the APB and UDB. These findings underscore that future ecological restoration and conservation efforts should implement coordinated, multi-service management mechanisms. Full article
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22 pages, 8105 KiB  
Article
Extraction of Sparse Vegetation Cover in Deserts Based on UAV Remote Sensing
by Jie Han, Jinlei Zhu, Xiaoming Cao, Lei Xi, Zhao Qi, Yongxin Li, Xingyu Wang and Jiaxiu Zou
Remote Sens. 2025, 17(15), 2665; https://doi.org/10.3390/rs17152665 - 1 Aug 2025
Viewed by 183
Abstract
The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract [...] Read more.
The unique characteristics of desert vegetation, such as different leaf morphology, discrete canopy structures, sparse and uneven distribution, etc., pose significant challenges for remote sensing-based estimation of fractional vegetation cover (FVC). The Unmanned Aerial Vehicle (UAV) system can accurately distinguish vegetation patches, extract weak vegetation signals, and navigate through complex terrain, making it suitable for applications in small-scale FVC extraction. In this study, we selected the floodplain fan with Caragana korshinskii Kom as the constructive species in Hatengtaohai National Nature Reserve, Bayannur, Inner Mongolia, China, as our study area. We investigated the remote sensing extraction method of desert sparse vegetation cover by placing samples across three gradients: the top, middle, and edge of the fan. We then acquired UAV multispectral images; evaluated the applicability of various vegetation indices (VIs) using methods such as supervised classification, linear regression models, and machine learning; and explored the feasibility and stability of multiple machine learning models in this region. Our results indicate the following: (1) We discovered that the multispectral vegetation index is superior to the visible vegetation index and more suitable for FVC extraction in vegetation-sparse desert regions. (2) By comparing five machine learning regression models, it was found that the XGBoost and KNN models exhibited relatively lower estimation performance in the study area. The spatial distribution of plots appeared to influence the stability of the SVM model when estimating fractional vegetation cover (FVC). In contrast, the RF and LASSO models demonstrated robust stability across both training and testing datasets. Notably, the RF model achieved the best inversion performance (R2 = 0.876, RMSE = 0.020, MAE = 0.016), indicating that RF is one of the most suitable models for retrieving FVC in naturally sparse desert vegetation. This study provides a valuable contribution to the limited existing research on remote sensing-based estimation of FVC and characterization of spatial heterogeneity in small-scale desert sparse vegetation ecosystems dominated by a single species. Full article
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20 pages, 4472 KiB  
Article
Exploring Scientific Collaboration Patterns from the Perspective of Disciplinary Difference: Evidence from Scientific Literature Data
by Jun Zhang, Shengbo Liu and Yifei Wang
Big Data Cogn. Comput. 2025, 9(8), 201; https://doi.org/10.3390/bdcc9080201 - 1 Aug 2025
Viewed by 158
Abstract
With the accelerating globalization and rapid development of science and technology, scientific collaboration has become a key driver of knowledge production, yet its patterns vary significantly across disciplines. This study aims to explore the disciplinary differences in scholars’ scientific collaboration patterns and their [...] Read more.
With the accelerating globalization and rapid development of science and technology, scientific collaboration has become a key driver of knowledge production, yet its patterns vary significantly across disciplines. This study aims to explore the disciplinary differences in scholars’ scientific collaboration patterns and their underlying mechanisms. Data were collected from the China National Knowledge Infrastructure (CNKI) database, covering papers from four disciplines: mathematics, mechanical engineering, philosophy, and sociology. Using social network analysis, we examined core network metrics (degree centrality, neighbor connectivity, clustering coefficient) in collaboration networks, analyzed collaboration patterns across scholars of different academic ages, and compared the academic age distribution of collaborators and network characteristics across career stages. Key findings include the following. (1) Mechanical engineering exhibits the highest and most stable clustering coefficient (mean 0.62) across all academic ages, reflecting tight team collaboration, with degree centrality increasing fastest with academic age (3.2 times higher for senior vs. beginner scholars), driven by its reliance on experimental resources and skill division. (2) Philosophy shows high degree centrality in early career stages (mean 0.38 for beginners) but a sharp decline in clustering coefficient in senior stages (from 0.42 to 0.17), indicating broad early collaboration but loose later ties due to individualized knowledge production. (3) Mathematics scholars prefer collaborating with high-centrality peers (higher neighbor connectivity, mean 0.51), while sociology shows more inclusive collaboration with dispersed partner centrality. Full article
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10 pages, 3612 KiB  
Communication
Comparison of Habitat Selection Models Between Habitat Utilization Intensity and Presence–Absence Data: A Case Study of the Chinese Pangolin
by Hongliang Dou, Ruiqi Gao, Fei Wu and Haiyang Gao
Biology 2025, 14(8), 976; https://doi.org/10.3390/biology14080976 (registering DOI) - 1 Aug 2025
Viewed by 103
Abstract
Identifying habitat characteristics is essential for conserving critically endangered species. When quantifying species habitat characteristics, ignoring data types may lead to misunderstandings about species’ specific habitat requirements. This study focused on the critically endangered Chinese pangolin in Guangdong Province, China, and divided the [...] Read more.
Identifying habitat characteristics is essential for conserving critically endangered species. When quantifying species habitat characteristics, ignoring data types may lead to misunderstandings about species’ specific habitat requirements. This study focused on the critically endangered Chinese pangolin in Guangdong Province, China, and divided the study area into 600 m × 600 m grids based on its average home range. The burrow number within each grid was obtained through line transect surveys, with burrow numbers/line transect lengths used as direct indicators of habitat utilization intensity. The relationships with sixteen environmental variables, which could be divided into three categories, including topographic, human disturbance and land cover composition, were quantified using the GAM method. We also converted continuous data into binary data (0, 1), constructed GAMs and compared them with habitat utilization intensity models. Our results indicate that the habitat utilization intensity model identified profile curvature and slope as primary factors, showing a nonlinear response to profile curvature (Edf = 5.610, p = 0.014) and a positive relationship with slope (Edf = 1.000, p = 0.006). The presence–absence model emphasized distance to water (Edf = 1.000, p = 0.014), slope (Edf = 1.709, p = 0.043) and aspect (Edf = 2.000, p = 0.026). The intensity model explained significantly more deviance, captured complex nonlinear relationships and supported higher model complexity without overfitting. This study demonstrates that habitat utilization intensity data provides a more ecologically informative basis for in situ conservation (e.g., identifying core habitats), and the process from habitat selection to habitat utilization should be integrated to reveal species’ habitat characteristics. Full article
(This article belongs to the Section Conservation Biology and Biodiversity)
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35 pages, 12322 KiB  
Article
Research on the Evaluation Method of Electrical Stress Limit Capability Based on Reliability Enhancement Theory
by Shuai Zhou, Kaixue Ma, Zhihua Cai, Shoufu Liu, Jian Xiang and Chi Ma
Electronics 2025, 14(15), 3056; https://doi.org/10.3390/electronics14153056 - 30 Jul 2025
Viewed by 128
Abstract
This study focuses on the evaluation of electrical stress limit capability for 3D-packaged memory (256 M × 72-bit DDR3 SDRAM) (Shanghai Fudan Microelectronics Group Co., Ltd., Shanghai, China). Guided by Reliability Enhancement Theory, this study presents a meticulously designed comprehensive test profile that [...] Read more.
This study focuses on the evaluation of electrical stress limit capability for 3D-packaged memory (256 M × 72-bit DDR3 SDRAM) (Shanghai Fudan Microelectronics Group Co., Ltd., Shanghai, China). Guided by Reliability Enhancement Theory, this study presents a meticulously designed comprehensive test profile that incorporates critical stress parameters, including supply voltage, input clock frequency, electrostatic discharge (ESD) sensitivity, and electrical endurance. Explicit criteria for stress selection, upper/lower bounds, step increments, and duration are established. A dedicated test platform is constructed, integrating automated test equipment (ATE) and ESD sensitivity analyzers with detailed specifications on device selection criteria and operational principles. The functional performance testing methodology is systematically investigated, covering test platform configuration, initialization protocols, parametric testing procedures, functional verification, and acceptance criteria. Extreme-condition experiments—including supply voltage margining, input clock frequency tolerance, ESD sensitivity characterization, and accelerated electrical endurance testing—are conducted to quantify operational and destructive limits. The findings provide critical theoretical insights and practical guidelines for the design optimization, quality control, and reliability enhancement of 3D-packaged memory devices. Full article
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24 pages, 623 KiB  
Article
Evaluation of Competitiveness and Sustainable Development Prospects of French-Speaking African Countries Based on TOPSIS and Adaptive LASSO Algorithms
by Binglin Liu, Liwen Li, Hang Ren, Jianwan Qin and Weijiang Liu
Algorithms 2025, 18(8), 474; https://doi.org/10.3390/a18080474 - 30 Jul 2025
Viewed by 222
Abstract
This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary [...] Read more.
This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary competitiveness were selected to quantitatively analyze the competitiveness of 26 French-speaking African countries. Results show that their comprehensive competitiveness exhibits spatial patterns of “high in the north and south, low in the east and west” and “high in coastal areas, low in inland areas”. Algeria, Morocco, and six other countries demonstrate high competitiveness, while Central African countries generally show low competitiveness. The adaptive LASSO algorithm identifies three key influencing factors, including the proportion of R&D expenditure to GDP, high-tech exports, and total reserves, as well as five secondary key factors, including the number of patent applications and total number of domestic listed companies, revealing that scientific and technological investment, financial strength, and innovation transformation capabilities are core constraints. Based on these findings, sustainable development strategies are proposed, such as strengthening scientific and technological research and development and innovation transformation, optimizing financial reserves and capital markets, and promoting China–Africa collaborative cooperation, providing decision-making references for competitiveness improvement and regional cooperation of French-speaking African countries under the background of the “Belt and Road Initiative”. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms (2nd Edition))
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16 pages, 3034 KiB  
Article
Interannual Variability in Precipitation Modulates Grazing-Induced Vertical Translocation of Soil Organic Carbon in a Semi-Arid Steppe
by Siyu Liu, Xiaobing Li, Mengyuan Li, Xiang Li, Dongliang Dang, Kai Wang, Huashun Dou and Xin Lyu
Agronomy 2025, 15(8), 1839; https://doi.org/10.3390/agronomy15081839 - 29 Jul 2025
Viewed by 143
Abstract
Grazing affects soil organic carbon (SOC) through plant removal, livestock trampling, and manure deposition. However, the impact of grazing on SOC is also influenced by multiple factors such as climate, soil properties, and management approaches. Despite extensive research, the mechanisms by which grazing [...] Read more.
Grazing affects soil organic carbon (SOC) through plant removal, livestock trampling, and manure deposition. However, the impact of grazing on SOC is also influenced by multiple factors such as climate, soil properties, and management approaches. Despite extensive research, the mechanisms by which grazing intensity influences SOC density in grasslands remain incompletely understood. This study examines the effects of varying grazing intensities on SOC density (0–30 cm) dynamics in temperate grasslands of northern China using field surveys and experimental analyses in a typical steppe ecosystem of Inner Mongolia. Results show that moderate grazing (3.8 sheep units/ha/yr) led to substantial consumption of aboveground plant biomass. Relative to the ungrazed control (0 sheep units/ha/yr), aboveground plant biomass was reduced by 40.5%, 36.2%, and 50.6% in the years 2016, 2019, and 2020, respectively. Compensatory growth failed to fully offset biomass loss, and there were significant reductions in vegetation carbon storage and cover (p < 0.05). Reduced vegetation cover increased bare soil exposure and accelerated topsoil drying and erosion. This degradation promoted the downward migration of SOC from surface layers. Quantitative analysis revealed that moderate grazing significantly reduced surface soil (0–10 cm) organic carbon density by 13.4% compared to the ungrazed control while significantly increasing SOC density in the subsurface layer (10–30 cm). Increased precipitation could mitigate the SOC transfer and enhance overall SOC accumulation. However, it might negatively affect certain labile SOC fractions. Elucidating the mechanisms of SOC variation under different grazing intensities and precipitation regimes in semi-arid grasslands could improve our understanding of carbon dynamics in response to environmental stressors. These insights will aid in predicting how grazing systems influence grassland carbon cycling under global climate change. Full article
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23 pages, 6132 KiB  
Article
Anthropogenic Activities Dominate Vegetation Improvement in Arid Areas of China
by Yu Guo, Xinwei Wang, Hongying Cao, Qin Peng, Yunshe Dong, Yunchun Qi, Jian Liu, Ning Lv, Feihu Yin, Xiujin Yuan and Mei Zeng
Remote Sens. 2025, 17(15), 2634; https://doi.org/10.3390/rs17152634 - 29 Jul 2025
Viewed by 147
Abstract
Arid regions, while providing essential ecosystem services, are among the most ecologically vulnerable worldwide. Understanding and monitoring their long-term vegetation dynamics is essential for accurate environmental assessment and climate adaptation strategies. This study examined the spatiotemporal variations and driving forces of the vegetation [...] Read more.
Arid regions, while providing essential ecosystem services, are among the most ecologically vulnerable worldwide. Understanding and monitoring their long-term vegetation dynamics is essential for accurate environmental assessment and climate adaptation strategies. This study examined the spatiotemporal variations and driving forces of the vegetation dynamics in arid Northwestern China during 2000 to 2020, using the annual peak fractional vegetation cover (FVC) as the primary indicator. The Sen’s slope estimator with the Mann–Kendall test and the coefficient of variation were employed to assess the spatiotemporal variations in FVC, while the Pearson correlation, geographic detector model and random forest model were applied to identify the dominant driving factors for FVC. The results indicated that (1) overall vegetation cover was low (averaged peak FVC = 0.191), showing a spatial pattern of higher values in the northwest and lower values in the southeast; high FVC values were primarily observed in mountainous areas and river corridors; (2) the annual peak FVC increased significantly at a rate of 0.0508 yr−1, with 33.72% of the region showing significant improvements and 5.49% degradation; (3) the spatial pattern of FVC was shaped by the distribution of land use types (59.46%), while the temporal dynamics of FVC were driven by land use changes (16.37%) and the land use intensity (37.56%); (4) both the spatial pattern and the temporal dynamics were limited by the environmental conditions. These findings highlight the critical role of anthropogenic activities in shaping the spatiotemporal variations in FVC, particularly emphasizing the distinct contributions of changes in land use types and land use intensity. This study could provide a scientific basis for sustainable land management and restoration strategies in arid regions facing global changes. Full article
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21 pages, 11816 KiB  
Article
The Dual Effects of Climate Change and Human Activities on the Spatiotemporal Vegetation Dynamics in the Inner Mongolia Plateau from 1982 to 2022
by Guangxue Guo, Xiang Zou and Yuting Zhang
Land 2025, 14(8), 1559; https://doi.org/10.3390/land14081559 - 29 Jul 2025
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Abstract
The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This [...] Read more.
The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article
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